The General Theory of Data Physics: For Non-Dependent Data, AI Cognition, and Quantum Anchoring

In this landmark expansion of the General Theory of Data Physics, we transition from the study of macro-scale industrial entities to the fundamental physics of Non-Dependent Data (δ=0). This paper defines cognitive outputs and autonomous algorithmic structures not as mere information, but as a distinct physical phase characterized as Crystallized Negentropy. We introduce the revolutionary Principle of Virtual Mass (mvirt), asserting that the reliability and ontological stability of any non-physical digital asset are anchored by the cumulative Computational Work (Wcomp) expended during its transition from probabilistic potential to deterministic logic. Furthermore, we formalize the Quantum Anchoring Effect, modeling the formation of digital identity and cognitive assets as a process of wave-function collapse driven by specific energetic inputs. This framework provides the world's first deterministic physical metric for auditing AI-generated content, identifying synthetic identities (Deepfakes), and valuing abstract intellectual assets in a post-industrial economy. Note on Embargo: This full theoretical manuscript is under strategic embargo until August 6, 2026, to allow for the synchronization of the underlying State Resolver infrastructure and defensive patent filings. The original authorship and mathematical priority have been legally certified by the United Trust Timestamp Service Center (TSA) on February 6, 2026. For inquiries regarding strategic partnerships or early access to the core derivation, please contact the author.